We employ the WRF-Chem model to study summertime air pollution,
the intense photochemical activity and their impact on air quality over the
eastern Mediterranean. We utilize three nested domains with horizontal
resolutions of 80, 16 and 4 km, with the finest grid focusing on the
island of Cyprus, where the CYPHEX campaign took place in July 2014.
Anthropogenic emissions are based on the EDGAR HTAP global emission
inventory, while dust and biogenic emissions are calculated online. Three
simulations utilizing the CBMZ-MOSAIC, MOZART-MOSAIC, and RADM2-MADE/SORGAM
gas-phase and aerosol mechanisms are performed. The results are compared with
measurements from a dense observational network of 14 ground stations in
Cyprus. The model simulates T2 m, Psurf, and
WD10 m accurately, with minor differences in WS10 m between
model and observations at coastal and mountainous stations attributed to
limitations in the representation of the complex topography in the model. It
is shown that the south-eastern part of Cyprus is mostly affected by
emissions from within the island, under the dominant (60 %) westerly flow
during summertime. Clean maritime air from the Mediterranean can reduce
concentrations of local air pollutants over the region during westerlies.
Ozone concentrations are overestimated by all three mechanisms
(9 % ≤ NMB ≤ 23 %) with the smaller mean bias (4.25 ppbV)
obtained by the RADM2-MADE/SORGAM mechanism. Differences in ozone
concentrations can be attributed to the VOC treatment by the three
mechanisms. The diurnal variability of pollution and ozone precursors is not
captured (hourly correlation coefficients for O3≤ 0.29). This
might be attributed to the underestimation of NOx concentrations by local
emissions by up to 50 %. For the fine particulate matter (PM2.5),
the lowest mean bias (9 µg m−3) is obtained with the
RADM2-MADE/SORGAM mechanism, with overestimates in sulfate and ammonium
aerosols. Overestimation of sulfate aerosols by this mechanism may be linked
to the SO2 oxidation in clouds. The MOSAIC aerosol mechanism
overestimates PM2.5 concentrations by up to
22 µg m−3 due to a more pronounced dust component compared to
the other two mechanisms, mostly influenced by the dust inflow from the
global model. We conclude that all three mechanisms are very sensitive to
boundary conditions from the global model for both gas-phase and aerosol
pollutants, in particular dust and ozone.

Many years of intense population growth have rendered the eastern
Mediterranean and the Middle East (EMME) region into a very densely populated
area, with more than 350 million inhabitants over an area with a 2000 km
radius. Strong industrialization and a lack of air pollution policy in the
countries in the region have resulted, in recent decades, in an increase of
anthropogenic emissions to the atmosphere. Compared to other regions in the
Northern Hemisphere, background concentrations of important trace gases and
aerosols over the EMME region are very high (Lelieveld et al., 2002), whilst the
Mediterranean Basin is found to be the region with the highest background
ozone (O3) levels in Europe. Several locations in the Middle East are
characterized by much higher nitrogen dioxide (NO2) column densities
than major cities in Europe (Lelieveld et al., 2009).

Downward transport from the upper troposphere and lower stratosphere
associated with enhanced subsidence and limited horizontal divergence has
been found to be another important mechanism, which increases the already
elevated O3 concentrations over the EMME region (Zanis et al., 2014). During
the summer period, the contribution of tropopause folds in mid-tropospheric
and lower tropospheric O3 concentrations is more significant over the
south-eastern Mediterranean (Akritidis et al., 2016). LRT also enhances carbon
monoxide (CO) surface concentrations, with 60 % to 80 % of the
boundary-layer CO over the Mediterranean attributed to polluted air
masses originating from western and eastern Europe, while the eastern
Mediterranean is mainly affected by emissions from Ukraine and Russia
(Lelieveld et al., 2002).

During the second phase of the Air Quality Model Evaluation International
Initiative (AQMEII), the nine working groups using the Weather Research and
Forecasting model coupled with chemistry (WRF-Chem) operationally reported an
overall underestimation of the annual surface ozone (O3) levels reaching
up to 18 % over Europe and 22 % over North America (Im et al., 2015b) with
autumn overestimation and winter underestimation. The meteorological and
chemical configurations of the different groups were found to have a
considerable effect on simulated O3 levels. Model performance was strongly
influenced from the boundary conditions, especially during autumn and winter.
Regarding particulate matter 2.5 µm or less in diameter
(PM2.5) concentrations, large overestimations over Europe were
reported (Im et al., 2015a). Tuccella et al. (2012) compared WRF-Chem model output
against ground-based observations over the European domain for the year 2007
with time-invariant boundary conditions. The model simulated temperature
satisfactorily with a small negative bias, but wind speed was highly
overpredicted. O3 daily maxima were underestimated, while mean O3
concentrations during spring (autumn) were underestimated (overestimated).
Ritter et al. (2013) applied the model over a Swiss domain for 2 years on a
2 km horizontal resolution. The model reproduced well temperature and solar
radiation, but failed to capture short-term peaks in pollutant concentrations
for several days.

In the literature various gas-phase chemistry and aerosol mechanisms have
shown different behaviour in terms of predicting the atmospheric
concentrations of pollutants over specific regions. Gupta and Mohan (2015)
compared the Carbon Bond Mechanism (CBM-Z) and the Regional Atmospheric
Chemical Model (RACM) gas-phase chemistry mechanisms over the mega city of
Delhi, India, at a horizontal grid resolution of 10 km for the innermost
model domain. Results showed that both mechanisms tend to overestimate O3
concentrations. It was noted that the use of a finer grid resolution may
improve the overall model performance. Mar et al. (2016) evaluated the
performance of the Regional Acid Deposition Model (RADM2) and MOZART-4
gas-phase chemistry mechanisms at a horizontal grid resolution of 45 km over
Europe. Simulated O3 consecrations by MOZART-4 were found to be up to
20 µg m−3 higher than RADM2 during the summer due to a higher
photochemical O3 production rate. On the other hand, RADM2 showed a
negative bias for the whole year, while both mechanisms slightly
underestimated nitrogen oxide (NOx) concentrations. Knote et al. (2014)
performed box-model intercomparison of several formulations for tropospheric
gas-phase chemistry under idealized meteorological conditions in the
framework of the second phase of AQMEII. They found significant variabilities
in the prediction of gaseous pollutants and key radicals and they highlight
that the choice of gas-phase mechanism is a crucial component in modelling
studies. Balzarini et al. (2015) showed that predicted total PM mass
concentrations as well as aerosol subcomponents vary between the MADE/SORGAM
and MOSAIC aerosol mechanisms. Differences were attributed to the approach
each mechanism uses to simulate the aerosol size distribution (modal or
sectional bin) and the gas-phase chemistry mechanisms these are coupled with
in the WRF-Chem model since they affect the concentrations of aerosol
precursors.

A very limited number of studies have dealt with online air quality
modelling over the EMME region, with apparent limitations.
Safieddine et al. (2014) employed the WRF-Chem model to study the tropospheric
O3 over the Mediterranean during the summer season at a horizontal grid
resolution of 50 km. The coarse model horizontal grid resolution was
proposed by the authors as a possible reason for model biases in their study.
Other studies in the region that utilize coupled meteorological and chemistry
models are usually short term. For example, Bossioli et al. (2016) carried out
WRF-Chem simulations for a limited time period focusing on the contribution
of biomass burning on PM levels. However they reported an increase in O3
levels by 50 % when boundary conditions from the MOZART-4 global model were
used. Kushta et al. (2014) highlighted the importance of natural aerosols when
simulating the photochemical state of the atmosphere during a dust episode in
April 2004.

In this study we employ and intercompare three coupled gas-phase chemistry
and aerosol mechanisms to study the long-range transport of air pollutants
and the intense photochemical activity over the eastern Mediterranean with
focus on the island of Cyprus, over the summer period, using high temporal
and spatial resolution down to 4 km. During July 2014, the CYprus PHotochemical EXperiment (CYPHEX)
campaign took place near Ineia, Paphos, a background measurement site on the
western coast of Cyprus, to investigate the photochemistry and air mass
transport of the eastern Mediterranean, providing us with an extensive
observation data set.

The paper is structured as follows.
In Sect. 2 we briefly describe the three gas-phase chemistry and aerosol
mechanisms used in the simulations, the basic model configuration including
the model domains, the common parameterizations and the emission data used.
In Sect. 3 we present the results from sensitivity tests dealing with the
effects of boundary conditions on the concentrations of gas-phase pollutants
and aerosols (Sect. 3.1).
We examine the ability of the model to predict the basic meteorological parameters
(Sect. 3.2), the concentrations of gas-phase pollutants (Sect. 3.3) and fine
particulate matter (Sect. 3.4).
Our conclusions are given in Sect. 4.

2.1 Gas-phase chemistry and aerosol mechanisms

The Weather Research and Forecasting (WRF) model is a state-of-the-art
regional meteorological model. Various gas-phase chemistry and aerosol
mechanisms have been implemented into the WRF model, creating the online
WRF-Chem model (Grell et al., 2005). In this study, we employ WRF-Chem version
3.61 with three widely used gas-phase chemistry and two aerosol mechanisms to
simulate air quality over the eastern Mediterranean:

CBMZ-MOSAIC (CM)

The lumped CBM-Z chemical mechanism (Zaveri and Peters, 1999) is
based on the Carbon Bond Mechanism (CBM-IV) developed by Gery et al. (1989). The
Carbon Bond Mechanism includes 73 chemical species and 237 reactions.
CBM-Z is coupled with the Model for Simulating Aerosol Interactions and Chemistry
(MOSAIC) developed by Zaveri et al. (2008). MOSAIC uses a sectional bin approach for
the representation of the aerosol size distribution. In the WRF-Chem model the user
can choose between four and eight size bins which are defined by their lower and
upper dry particle diameters. In both cases, only one bin is dedicated to aerosols
with diameter between 2.5 and 10 µm. Therefore, when four aerosol bins are
used, three bins are dedicated to aerosols less than 2.5 µm in diameter, and
when eight aerosol bins are used, seven bins are dedicated to aerosols with diameters within this range.
Since this study focuses on the total PM2.5 mass concentrations and not
on detailed aerosol microphysics or effects on clouds, it is sufficient to use the
four-bin option to reduce computational complexity.

MOZART-MOSAIC (MM)

The MOZART gas chemical mechanism, developed
by Emmons et al. (2010), is also used coupled with the MOSAIC aerosol scheme.
It includes 85 chemical species and 196 reactions and is consistent with the
chemistry used in the global model that provides the chemical boundary
conditions for our simulations.
The MOZART mechanism has been widely used
with WRF-Chem for simulations outside Europe, but only a limited number of
studies have applied it over the European domain.

RADM2-MADE/SORGAM (RMS)

The second generation Regional Acid Deposition
Model (RADM2) chemical mechanism for regional air quality modelling
(Stockwell et al., 1990) includes 59 chemical species and 157 reactions. RADM2
is a widely used mechanism over the European domain and it is coupled with
the Modal Aerosol Dynamics for Europe (MADE) (Ackermann et al., 1998). MADE
uses a modal approach for aerosol treatment and is coupled with the
Secondary Organic Aerosol Model (SORGAM) (Schell et al., 2001). SORGAM is
capable of simulating secondary organic aerosol (SOA) formation including
the production of low-volatility products and their subsequent gas–particle
partitioning.

2.2 Model configuration

All our simulations are conducted with the same model physics configuration
(Table 1) to facilitate intercomparison. We modified the
WRF-Chem v3.6.1 code to take into account dust particles in the accumulation
size mode (0.1 µg m−3≤ particle size ≤ 2.5 µg m−3)
for the calculation of the total PM mass concentration in
the RMS mechanism. Three nested domains are used, as shown in
Fig. 1a. The outermost domain (d1) with a
horizontal grid resolution of 80 km extends from 16 to 4∘ N
and from 10∘ W to 50∘ E in order to include a large part of
Europe and the Black Sea region, which have a significant contribution to the
pollution of air masses that reach the EMME region, as well as a large part
of the Sahara and Middle Eastern deserts in order to utilize the dust emission
schemes included in the WRF-Chem model. The second domain (d2) with a
horizontal grid resolution of 16 km is located over the Levantine Basin,
including all the surrounding major urban centres. The third innermost domain
(d3) is located over the island of Cyprus (Fig. 1b) with a horizontal grid resolution of 4 km, allowing for a more accurate
representation of the state of the atmosphere over the complex terrain of the
island close to the surface observation stations. The WRF-Chem model uses a
terrain-following hydrostatic-pressure vertical coordinate system. In our
study 29 layers are used from the surface up to 50 hPa. The first layer on
average extends to a height of 70 m. Control experiments that were conducted
during the model set-up showed that increasing the number of vertical layers
(in the lowest 70 m or throughout the vertical extent of the atmosphere)
does not significantly alter the concentrations of pollutants near the
surface, at the station locations, due to mixing within the boundary layer.

Meteorological initial and boundary conditions are provided by the Global
Forecast System (GFS) at a horizontal grid resolution of 0.25∘×0.25∘. Time-variant chemical boundary conditions are provided
from the global Model for OZone And Related chemical Tracers (MOZART-4;
Emmons et al., 2010). The MOZART-4 model output datasets are available at a
horizontal grid resolution of 1.9∘×2.5∘ and
interpolated in space every 6 h to our model domain and the chemical
species of each mechanism. Biogenic emissions are calculated online by the
the Model of Emissions of Gases and Aerosols from Nature version 2.1
(MEGAN2.1) by Guenther et al. (2012). We use the Air Force Weather Agency
(AFWA) dust scheme that was developed based on the Marticorena and Bergametti (1995)
dust emission scheme in the Goddard Global Ozone Chemistry Aerosol Radiation
and Transport (GOCART) model (Chin et al., 2000). The EDGAR-HTAP (Emission
Database for Global Atmos. Res. for Hemispheric Transport of Air
Pollution) Version 2, compiled by the European Commission, Joint Research
Center (JRC)/Netherlands Environmental Assessment Agency (LRTAP-Wiki, 2014) is
utilized. This dataset includes emissions of gaseous pollutants such as sulfur
dioxide (SO2), NOx, CO, non-methane volatile organic
compounds (NMVOCs) and ammonia (NH3) and particulate matter with
carbonaceous speciation (PM10; PM2.5; black carbon, BC;
and organic carbon, OC) from anthropogenic and biomass burning sectors
(Janssens-Maenhout et al., 2012).
PM2.5 is a subset of PM10 and includes BC, OC,
SO42-, NO3-, crustal material, metal, and other dust
particles. The dataset used in this study is available in 0.1∘× 0.1∘
emission grid maps for the year 2010 and can be downloaded from the EDGAR JRC website
per year, per substance, and per sector. Anthropogenic emissions were interpolated in
space and time to produce daily emissions using the anthro_emiss utility
(Kumar, 2017).

2.3 Observational data

The model output is compared against observational data from a dense station
network which spans the island of Cyprus and covers a large variety of
monitoring sites, including seaside and mountainous areas. Specifically, the
modelled meteorology is compared against meteorological hourly observations
from eight ground stations operated by the Cyprus Department of Meteorology
(Fig. 1, squares), and meteorological data from the CYPHEX
campaign, which took place from 7 to 31 July 2014, near the village of Ineia (Fig. 1, star). Modelled pollutant concentrations are
compared against observational data from five background air quality
monitoring ground stations operated by the Cyprus Department of Labour
Inspection – DLI (Fig. 1, circles) and data from the CYPHEX
campaign. The Finokalia station in Crete, which is part of the European
Monitoring and Evaluation Programme (EMEP) network, is used as a reference
station to discuss O3 discrepancies on measurements over Cyprus. The
frequency of all air pollutant concentrations measurements is hourly, except
for PM2.5 concentrations measurements by the CYPHEX campaign, which
are provided every 6 h. The location of the air pollution and
meteorology monitoring stations and the measurements carried out at each
station are given in detail in Table 2.

3.1 Boundary condition sensitivity tests

Previous studies have shown that lateral boundary conditions affect the
modelled near-surface O3 concentrations. Akritidis et al. (2013) highlighted
the importance of time variant chemical boundary conditions on O3
concentrations over Europe. Kushta et al. (2017) (accepted for publication)
showed that chemical boundary conditions from the MOZART-4 global model have
an important effect on the modelled concentrations over the region of study.
More specifically, in their study, an important O3 overestimation by the
WRF-Chem model was attributed to the effect of chemical boundary conditions.
When the O3 inflow from the boundaries was reduced by 30 %, model
results were closer to observations. Based on these results and the MOZART-4
model evaluation (Emmons et al., 2010), O3 inflow from the global model was
reduced by 30 % in our study. Figure 2a shows the
observed O3 concentrations at the five air quality stations and the CYPHEX
campaign and the modelled concentrations from (a) the base run using the RMS
mechanism (blue) and (b) a simulation where initial O3
concentrations and O3 inflow from the global model were reduced by 30 %
(red). The average NMB decreased from 21 to 9 % when O3 from the
global model was reduced. Similar results appear at the Finokalia background
station, where NMB was reduced from 18 to 7 % when O3 inflow from the
global model was reduced by 30 %. The results for the CM and MM mechanisms
are analogous. O3 overestimation due to the effect of boundary conditions
from the MOZART-4 model was also reported by Abdallah et al. (2016). In their
study, the Polyphemus chemical transport model was found to highly
overestimate O3 concentrations over Lebanon when using boundary conditions
from the MOZART-4 model. Bossioli et al. (2016) also reported a significant
contribution of boundary conditions over the O3 levels in the area.
Therefore, O3 from the global model was reduced by 30 % for the
simulations of this study.

Since dust is an important parameter of air quality in the region of study
and important dust sources are not included in our outermost domain, dust
inflow from the global model was taken into account in our simulations. The
effect of dust from the boundaries by the MOZART-4 on the WRF-Chem
PM2.5 concentrations is examined. In the CM mechanism much higher
PM2.5 concentrations than those observed occur between 11 and
13 July. To investigate this discrepancy we performed two CM simulations
(Fig. 3) with (continuous blue line) and without
(dashed blue line) dust influx from the boundaries.

Incoming dust results in an increase of the order of 19 % in
PM2.5 modelled concentrations during the whole study period. This
increase is more pronounced from 11 to 13 July (40 %). Dust presence
also influences O3 concentrations though aerosol–radiation feedbacks and
their impact on photolysis rates. Figure 2b shows
the observed and modelled O3 concentrations with (blue markers) and
without (red markers) dust influx from the global model. The inclusion of
dust particles results in a decrease of 10 % in modelled O3
concentrations due to changes in solar radiation through aerosol–radiation
interactions.

Figure 3Observed (black markers) and modelled PM2.5 concentrations
from the CM (blue line), MM (red line), and RMS (green line) mechanisms. The
dashed blue line represents the CM simulation without dust influx from the
global model. All model simulation output and observations are given in
hourly resolution, expect for the CYPHEX campaign measurements, which are
provided in 6-hourly resolution.

The comparison of model to station data is performed using the first free
model layer. The actual altitude of four stations, located in regions with
very complex terrain, was found to differ from the model terrain height
(CYPHEX campaign, Ineia village, Troodos air quality monitoring station, and
the nearby Prodromos meteorological station). As a test, we performed the
comparison using modelled concentrations taking into account the actual
altitude of the stations. This resulted only in a slightly better agreement
in the predicted surface pressure at the CYPHEX campaign and the Prodromos
station. Results in all other locations were not influenced because of the
mixing within the model boundary layer.

3.2 Meteorology

We evaluate the model performance regarding basic meteorological parameters.
Statistical metrics are derived by comparing the output of the three model
simulations to hourly measurements at ground stations. Pearson's correlation
coefficient (R), mean bias (MB), normalized mean bias (NMB), and root mean
squared error (RMSE) for temperature at 2 m (T2 m), wind speed at 10 m
(WS10 m), and surface pressure (Psurf) averaged over all
stations are shown on Table 3. Meteorology statistical
metrics for individual stations can be found in the Supplement (Table S1).
Modelled T2 m is in good agreement with observations (NMB = 2 to
3 %) with similar RSME values (2.72 to 2.78 ∘C) for all three
mechanisms. The diurnal cycle of T2 m is reproduced at the majority
of the stations (R∼0.66). The model though does not capture the
T2 m diurnal variability at the Larnaca meteorological station (R<0.20). The station is very close to the sea and the Larnaca Salt Lake, which
might influence the thermal circulation in the area.

The model tends to overestimate WS10 m at the majority of the
stations by an average of 1.71 to 1.83 m s−1 (R∼ 0.46) for all
three mechanisms. Mar et al. (2016) and Zhang et al. (2013) also reported
WS10 m overpredictions by the WRF model over the Mediterranean. The
latter study attributed this model behaviour to the poor representation of
surface drag exerted by the unresolved topography (mountains, hills and
valleys) and other smaller scale terrain features.

Local circulation is successfully predicted by the model.
Figure 1a shows the average 10 m wind direction from 12:00
to 17:00 LST in green and from 00:00 to 05:00 LST in red. The model
simulates sea breezes during daytime and katabatic winds during the night in
agreement with observations. The wind roses at the Athalassa station from the
observational data
(Fig. 4a top left panel) and the three simulations show that
wind direction is reproduced quite well by the model (Fig. 4).
The inland dominating wind direction is mainly westerly and north-westerly
with frequency of occurrence 60 and 20 % respectively. Similar results
appear for the majority of the stations (not shown here). Some discrepancies
between model and observations at the Prodromos station are attributed to the
complex mountainous topography of the Troodos area. Both model simulations
and observational data reveal predominant south-westerly winds at the
southern coastline of the island during day and night. The summertime general
circulation pattern over the eastern Mediterranean with predominant northerly
and westerly winds, as well as the anticyclonic flow over western Africa
(Fig. 1a), is also resembled by the model.

Figure 4Wind roses (monthly mean wind speed and direction at 10 m) at the
Athalassa meteorological station (a), and from the CM (b), MM (c),
and RMS (d) simulations.

There is very good agreement between observed and modelled Psurf
with high hourly correlation coefficients (R≥0.87) and normalized mean
bias of 1 %. Some negligible discrepancies exist between the three
mechanisms. The differences in the meteorological components are attributed
to the inclusion of the aerosol–radiation feedbacks in the simulations. The
model performance regarding aerosol concentrations is discussed later in the
paper.

3.3 Main gaseous pollutants

Observed average monthly O3 concentrations for July 2014 fall within the
climatological averaged summer values given by Kleanthous et al. (2014). In
their study, July monthly means of O3 concentrations over a period of
15 years were found to be 54.3 ± 4.7 ppbV over all stations. The mean
observed value during our simulation period at the DLI stations is 52 ppbV.
The mean modelled values vary from 56.2 ppbV (NMB = 9 %) in the RMS
mechanism to 63 ppbV (NMB = 22 %) and 65.2 ppbV (NMB = 23 %) in
the CM and MM mechanism respectively, showing a strong overestimation of the
latter two. Figure 5 shows the average O3 ground-level modelled
concentrations for the three mechanisms for the outermost domain (Europe –
Mediterranean and North Africa). Differences between the three mechanisms are
more pronounced over southern Europe and the Mediterranean. Over these
regions O3 concentrations predicted by the MM mechanisms are up to 10 and
20 ppbV higher compared to the CM and RMS mechanisms respectively.

The CYPHEX campaign station has been excluded from the analysis of O3.
This station gives significantly higher average monthly O3 concentration
(71.40 ppbV) that deviates from the climatological and observed mean, even
though the station of the campaign was located only a few hundred metres away
from the Ineia site of DLI (51.93 ppbV). A direct comparison between these
two stations is shown on Fig. S1 in the Supplement. We further investigated
this deviation by comparing with the mean monthly O3 for July 2014 at the
Finokalia station (from the European Monitoring and Evaluation Programme –
EMEP), which reaches 52.43 ppbV. We choose Finokalia because it is located on
the island of Crete (approximately 600 km west of Ineia) with no pollution
sources in between (Fig. 1a, red star). Thus
Ineia and Finokalia have similar pollution features, being subject to air
mass transport from eastern Europe.

Table 4 shows the statistical performance of the three
gas-phase and aerosol mechanisms against hourly observations from six ground
stations. The CM and MM mechanisms significantly overestimate O3
concentrations with normalized mean biases of 22 and 23 % respectively. A
normalized mean bias of 9 % appears for the RMS mechanism, which corresponds
to 4.25 ppbV. This mechanism shows the lowest RMSE (10.77 ppbV) compared to
CM and MM (14.79 and 15.30 ppbV respectively), which is a significant
improvement compared to the global MOZART-4 model (NMB = 37 %,
RMSE = 20.96 ppbV). A further improvement of the order of 3 % is shown
when moving from the coarse to the finer WRF-Chem domain on O3
concentrations (Supplement Table S2, Fig. S4).

Air quality modelling studies over the eastern Mediterranean in the
literature focus on O3. During the second phase of the Air Quality Model
Evaluation International Initiative (AQMEII), the majority of the modelling
groups using the RMS and CM mechanisms with the WRF-Chem model also reported
O3 concentrations overestimation over the eastern Mediterranean
(Im et al., 2015a). However, Mar et al. (2016) reported an underestimation
of about 5 ppbV on summertime O3 concentrations WRF-Chem model using the
RMS mechanism.

Our sensitivity tests showed that high PM2.5 concentrations affect
the O3 concentrations through the aerosol–radiation feedbacks by altering
the radiation budget and as a consequence, the photochemical activity and the
concentration of secondary pollutants. Specifically, when the dust inflow
from the boundaries for the CM mechanism was not taken into account, O3
concentrations at the stations locations increased by 10 %. Since the CM
mechanism shows the higher PM2.5 concentrations and relatively high
O3 concentrations, we conclude that we can rule out the aerosol
concentrations and therefore the different aerosol mechanisms, as the
responsible factor for the differences in O3 concentrations between the
three simulations. Knote et al. (2014) attributed the differences in pollutants
concentrations between these mechanisms to the differences in the treatment
of VOCs, since the rate constants for the basic O3 production and loss
reactions are similar. Hourly correlation coefficients for O3 are low
(less than 0.30) for all three mechanisms which is comparable to the findings
of Mar et al. (2016). In their study, summertime O3 hourly correlation
coefficients at the Ayia Marina station (AQ01) were close to 0.2. The low
correlation coefficients for O3 in combination with underestimated NOx
concentrations (NMB varies from −54 to −44 %) and low hourly
correlation coefficients for NOx as well suggest that nearby
anthropogenic emission sources are not represented in the emission inventory
used in the simulations. The monthly average diurnal cycles for O3 and
NOx (Figs. S2 and S3 respectively) show a weak diurnal cycle from
observational data at the majority of the stations, indicating that
long-range transport is an important aspect of air quality over Cyprus. A
more pronounced diurnal cycle for NOx appears at the Stavrovouni station
due to the fact that the station is located close to the highway.

The hourly modelled and observed O3 concentrations at six background
stations over Cyprus are depicted in Fig. 6. The three
mechanisms show similar behaviour for 1 to 13 July. However, from 13 July
until the end of the month O3 concentrations from CM and MM appear
slightly higher than RMS. The fact that such differences do not appear for
O3 precursors NOx (Fig. 7) indicates that the
different behaviour during this period is possibly due to aerosol–radiation
interactions and changes in photolysis rates. Similar patterns appear for
CO (Fig. 8). There is a good agreement between the
three mechanisms from
1 to 13 July. The RMS mechanism gives higher CO
concentrations for the rest of the simulation period. Due to the long
CO atmospheric residence time, these differences can be attributed to
long-range transport, and therefore the effect the three different gas-phase
chemistry and aerosol mechanisms have on the predicted meteorology.

An abrupt decrease in O3 concentrations in observations from 11 to 13 July
is also captured by all three mechanisms at all stations except Cavo Greco,
which is located in the eastern part of the island
(Fig. 6). This decrease in O3 concentrations is
accompanied by a decrease in CO concentrations as demonstrated from
the observational data from the CYPHEX campaign and the WRF-Chem model. No
abrupt changes are shown in NOx concentrations either by the observational
data or the model. During this period model results reveal that westerlies
account for more than 70 % at the stations where decreases in O3 and
CO concentrations occur, indicating the transfer of cleaner maritime
air from the Mediterranean. In general, wind direction appears to have an
important impact on pollutant concentrations over Cyprus.
Kleanthous et al. (2014) showed that at the Ayia Marina station northerlies are
associated with 3–5 % higher O3 concentrations compared to westerlies
and southerlies during all seasons. Similar results appear for modelled O3
concentrations at this station. More specifically, northerlies are associated
with 4–12 % higher O3 concentrations compared to westerlies and
southerlies for July 2014.

All three mechanisms tend to underestimate NOx concentrations at the
majority of the stations (NMB varies from −53 to −44 %). The Ayia Marina
and the Troodos stations are located in a mountainous region which is
characterized by steep changes in altitude within short distances. The
complexity of the terrain results in inaccuracies in the representation of
the local wind circulation by the model, which affects the transport of
pollutants. This is also supported by the CO model underestimation at
the Ayia Marina station. Modelled NOx concentrations are
significantly higher, and in better agreement with observations at the Cavo
Greco and Stavrovouni stations. The latter is located a few kilometres to the
east of an industrial area, which is represented in the anthropogenic
emission inventory used in the simulations. On the other hand, a large nearby
highway is not captured by the anthropogenic emission inventory, resulting in
peaks in NOx concentrations from traffic, which are not captured by
the model. The Cavo Greco station is located in the eastern part of the
island. Model results showed that when westerlies occur at this location,
NOx concentrations are significantly higher compared to southerlies
for all three mechanisms, indicating that the eastern part of Cyprus is
affected by transported pollutants, which are emitted within the island.
Average NOx concentrations during northerlies are also higher
compared to southerlies since the station is located south of the main power
station of the island (Dekeleia).

3.4 Fine particulate matter (PM2.5)

As shown previously in Fig. 3, all three mechanisms
overestimate PM2.5 concentrations at the Ayia Marina station and
the CYPHEX campaign site. The lowest MB appears for MADE/SORGAM
(MB = 8.96 µg m−3),
whereas the MB for MOSAIC is 22.37 µg m−3
when coupled with CBMZ and 16.98 µg m−3 when coupled with MOZART.

Differences in PM2.5 concentrations between the three simulations
are pronounced from 11 to 13 July. During this period, the
average PM2.5 concentrations at the Ayia Marina station were 93.95, 53.81,
7.17 µg m−3 for
CM, MM, and RMS respectively, whereas the average PM2.5
concentration from observational data was 13.72 µg m−3.

Investigating the inorganic mass in the MOSAIC aerosol mechanism, which
represents fine dust particles (Zaveri et al., 2008), we find the overestimation
is drve by the dust component. Since observations do not show elevated
aerosol levels near the surface from 11 to 13 July, the
dust component has been subtracted from the simulated total PM2.5
concentrations (Fig. 9). PM2.5
concentrations were significantly reduced and are in better agreement with
observations for CM and MM simulations, especially from 11 to
13 July. This indicates that in the MOSAIC aerosol mechanism dust has
a great contribution to PM2.5 concentrations during this period. On
the other hand, smaller differences are shown for the MADE/SORGAM aerosol
mechanism. The large difference in PM2.5 from mid-month coincides
with the time frame where large discrepancies in gaseous pollutants occur
between the three mechanisms, as discussed in Sect. 3.3. This underlines
the importance of the interactions between aerosols and radiation and
consequently photolytic reactions and air quality.

Figure 9Time series of observed and modelled PM2.5 concentrations
from the CM, MM, and RMS mechanisms (a) and scatter plots (b) at the
Ayia Marina station and the CYPHEX campaign. The dust component from the
modelled concentrations has been removed from all simulations.

In order to better understand individual components of PM2.5, and
examine differences in behaviour by the aerosol mechanisms, we analyse the
aerosol species that dominate the PM2.5 concentrations separately.
Figure 10 presents the components from observed and modelled
sulfate (SO42-), ammonium (NH3+), and nitrate
(NO3-) aerosol mass concentrations and elemental carbon
concentrations at the Ayia Marina station during the study period. Monthly
mean sulfate aerosol concentrations for the three mechanisms vary from 5.14
to 7.02 µg m−3, which is close to observed
values (5.05 µg m−3). The lowest monthly mean concentrations are
produced by the CM mechanism. This mechanism shows the highest sulfate
dioxide (SO2) concentrations during the whole study period (Fig. 11).
Since this simulation uses the same aerosol mechanism
with the MM simulation, and therefore the same heterogeneous nucleation rates
from sulfuric acid (H2SO4) to sulfate aerosols, the differences
between the CM and MM simulations are attributed to the chemical processes
that act as sources/sinks of H2SO4. The RMS mechanism includes the
heterogeneous SO2 cloud oxidation (de Brugh et al., 2011; Balzarini et al., 2015),
which results in higher sulfate aerosol concentrations compared to CM and
MM.

Elemental carbon is underestimated by all three simulations. The lowest NMB
appears for RMS (−34 %) followed by CM (−35 %). Since the three simulations
use the same anthropogenic emission inventory, these differences between RMS
and MM can be partially attributed to the different treatment of aerosols by
the modal and sectional bin approaches. The MM mechanism highly
underestimates EC concentrations due to the absent of anthropogenic emissions
in this mechanism.

Ammonium aerosol mean monthly values are close to observed 1.43 µg m−3 for
the CM and MM simulations (1.74 and 1.87 µg m−3 respectively),
while a higher value is shown for RMS (3.24 µg m−3). Nitrate
aerosols are highly overestimated by the RMS
mechanism with maximum values and outliers well above the period average.
These outliers are due to nitrate aerosol transport from the north, when
favoured by wind speed and direction. In contrast, nitrate aerosols are
slightly underestimated by CM and MM. These differences lie in the differrent
treatment of the gas-to-particle partitioning from the nitric acid to
ammonium nitrate as a function of humidity from the two aerosol mechanisms
used (Balzarini et al., 2015). MADE uses the Mozurkewich (1993) approach and
MOSAIC uses the Zaveri et al. (2008) method. The diurnal cycle of compounds that
are crucial to night-time chemistry (NO3, N2O5) vary
significantly between the three mechanisms (not shown). The RMS mechanism
exhibits 3 times higher night-time NO3 concentrations than the CM
mechanism. This indicates considerable uncertainty in the representation of
this important part of tropospheric chemistry that also affects aerosol
formation. These results are supported by the findings of Knote et al. (2014)
that also report 3 times higher pan-European averaged NO3 in the
RADM2 mechanism compared to CBMZ in the middle of the night-time chemistry
cycle.

Figure 10Box-and-whisker plots of observed and modelled sulfate, ammonium,
and nitrate aerosols and elemental carbon at the Ayia Marina station for
July 2014.

We simulated atmospheric gases and aerosols using the WRF-Chem model over the
eastern Mediterranean during the summer. The performance of three gas-phase
chemistry and aerosol mechanisms is investigated during the CYPHEX campaign
in July 2014. Model output is compared with meteorological and air quality
observational data from 14 ground stations. The model reproduces the
summertime synoptic wind circulation over the region and the local
circulation. It overestimates wind speed at the majority of the stations by
an average of 1.71 to 1.83 m s−1. Near-surface temperature and pressure
are reproduced accurately both in magnitude and diurnal variation. Some
discrepancies in modelled and observed meteorological parameters may be
attributed to the limited representation of the topography by the model.

Monthly average concentrations of O3 are overestimated by the CM and MM
mechanisms by 22 and 23 % respectively, whereas a small underestimation is
obtained by RMS (9 %). Sensitivity tests showed that PM2.5
concentrations can affect secondary pollutant formation though
aerosol–radiation feedbacks. A decrease of the order of 19 % in
PM2.5 concentrations, as a result of setting the dust inflow from
the global model to zero, resulted in 10 % increase in O3
concentrations. The differences in O3 concentrations are attributed to the
different treatment of VOCs as suggested by Knote et al. (2014). Hourly
correlation coefficients are low for all three mechanisms. NOx
concentrations do not differ between the simulations, with underestimation at
the majority of the stations, suggesting that nearby/local anthropogenic
emission sources are not well represented in the emission inventory.

Differences in O3 and CO concentrations between the three
simulations, during the second half of the simulation period, are attributed
to differences in meteorology that derive from the aerosol–radiation
interactions. Concurrent abrupt decreases in O3 and CO
concentrations (observations and model) during specific days are accompanied
by dominance of westerlies carrying clean maritime air from the
Mediterranean. Northerlies at the Ayia Marina station are associated with
4–12 % higher O3 modelled concentrations compared to westerlies and
southerlies for July 2014, which is in good agreement with previous studies.

The terrain complexity in the mountainous areas is the reason for the
inaccuracies in the representation of the local wind circulation by the model
that affects the transport and vertical mixing of pollutants. As a result,
the model performance at these stations (Ayia Marina, Troodos) regarding all
pollutants is less satisfactory. On the other hand, the model performance is
better in locations with less complex terrain such as the Stavrovouni and
Cavo Greco stations. Increased NOx concentrations at the Cavo Greco
station when westerlies occur indicate that the eastern part of Cyprus is
affected by emission sources located on the island.

The model skill to reproduce PM2.5 concentrations is examined. The
MOSAIC aerosol mechanism highly overestimates PM2.5 concentrations
(NMB ≥ 100 %). When the dust component is subtracted from the total
PM2.5 concentrations from all mechanisms there is a better
agreement with observations. The RMS mechanism slightly overestimates
sulfate and ammonium aerosol at the Ayia Marina station. The CM and MM
modelled concentrations of these species are closer to observations
(NMB = 2
and 34 % respectively). The lowest sulfate aerosol concentrations are
produced by the CM mechanism and are accompanied by higher SO2
concentrations. The differences between the two simulations using the MOSAIC
aerosol mechanism may be attributed to the chemical processes that act as
sources/sinks of SO2. The inclusion of the heterogeneous SO2
cloud oxidation in the RMS mechanism results in higher sulfate aerosol
concentrations (NMB = 38 %), as described in de Brugh et al. (2011) and
Balzarini et al. (2015). Elemental carbon is underestimated by all three
mechanisms indicating lack of emission sources. Differences between the RMS
(NMB =−34 %) and the CM (NMB =−34 %) mechanisms are attributed to
the different approach for the simulation of the aerosol size distribution.
Observed and modelled (by CM and MM) nitrate aerosol concentrations at the
Ayia Marina site are negligible. RMS simulations yield higher values,
probably attributed to nitrate aerosol formation upwind of the measurement
site. It is found that key night-time compounds like NO3 and
N2O5 differ significantly between the three mechanisms.

We conclude that all three mechanisms are very sensitive to boundary
conditions from the global model for both gas-phase and aerosol pollutants.
Care has to be taken, for ozone in particular, which has an important impact
on the modelled gas-phase pollutants for all mechanisms. In addition, dust
has a great contribution to PM2.5 concentrations from the MOSAIC
aerosol mechanism, while the corresponding concentrations from CBMZ-MOSAIC
were found to be very sensitive to dust from the boundaries.

The authors wish to thank the CYprus PHotochemical EXperiment (CYPHEX)
campaign, the Cyprus Department of Meteorology (DoM) and the Cyprus
Department of Labour Inspection (DLI), as well as the EMEP network for providing the observational data
used for model evaluation in this study.
Plots and diagrams were produced using the NCAR Command Language (NCL) version 6.3.0
(http://www.ncl.ucar.edu/), the openair R package (Carslaw and Ropkins, 2012), and Qtiplot (http://www.qtiplot.com/).
The Computational resources and support were provided
by the European Union Horizon 2020 research and innovation programme VI-SEEM
project under grant agreement 675121.

Tyrlis, E., Lelieveld, J., and Steil, B.: The summer circulation over the
eastern Mediterranean and the Middle East: Influence of the South Asian
monsoon, Clim. Dynam., 40, 1103–1123, https://doi.org/10.1007/s00382-012-1528-4,
2013. a

We investigate the impact of the choice of gas-phase and aerosol mechanisms, on the simulated summertime concentrations of several pollutants over the eastern Mediterranean, using the WRF-Chem model. The selection of mechanisms significantly affects ozone and fine particulate matter concentrations, and to a lesser extent other gaseous pollutants (NOx, CO). Meteorological components are also affected by the choice of mechanisms due to the interaction of aerosols with radiation.

We investigate the impact of the choice of gas-phase and aerosol mechanisms, on the simulated...